Kate Culhane, Dept. of Ecology, Evolution, and Marine Biology, UC Santa Barbara
kathrynculhane@ucsb.edu
SUMMARY
Exploring the data
DEPENDENCIES
##### PACKAGES #####
library(tidyverse) # data manipulation & visualization
library(vegan) # community analyses
library(goeveg) # scree plot for NMDS
##### DATA #####
# Invertebrate counts by order from sticky and pitfall trap samples
prey <- read_csv("output/02_prey.csv")
# Invertebrate counts by order from lizard stomach pumping samples
diet <- read_csv("output/02_diet_inverts.csv")
# Site by species matrix (invert prey data)
prey_matrix <- read_csv("output/02_prey_matrix.csv")
# Site by species matrix (diet data)
diet_matrix <- read_csv("output/02_diet_matrix.csv")Basics
Abundance by order
Available prey community
# Summary table
prey %>%
group_by(order, trap_type) %>%
summarise(n = sum(count)) %>%
pivot_wider(names_from = trap_type, values_from = n) %>%
mutate(total = pitfall + sticky) %>%
arrange(-total)# Abundance by order
prey %>%
ggplot(aes(x = trap_type, y = count)) +
geom_jitter(aes(color = trap_type),
width = 0.1, size = 1.5) +
geom_boxplot(fill = NA, outlier.shape = NA) +
facet_wrap(~ fct_reorder(order, count, .fun = sum, .desc = TRUE),
scales = "free_y") +
theme_classic() +
theme(legend.position = "NA") +
labs(x = "Trap type", y = "Abundance (indv/trap)")
- Each point represents a trap (12 trap pairs/site, 9 sites)
Diet
# Summary table
diet %>%
group_by(order) %>%
summarise(n = sum(count)) %>%
arrange(-n)# Abundance by order
diet %>%
# Data wrangling
group_by(order) %>%
mutate(n = length(count)) %>%
# Plot
ggplot(aes(x = fct_reorder(order, count, .fun = sum, .desc = TRUE), y = count)) +
geom_jitter(width = 0.1, size = 1.5) +
geom_boxplot(fill = NA, outlier.shape = NA) +
geom_text(aes(y = 10, label = paste("n =", n)),
stat = "unique", angle = 90, size = 3, hjust = 0) +
scale_y_continuous(expand = c(0,0),
limits = c(0,11.5),
breaks = c(0,2,4,6,8,10)) +
theme_classic() +
theme(legend.position = "NA",
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1)) +
labs(x = "Order", y = "Abundance (indv/lizard)")
- Each point represents a stomach sample from a single lizard (148 lizards)
- Four outliers not plotted for hemiptera: 48, 22, 22, 17
NMDS
Invert trap data
# Wrangle matrix
m_p <- as.matrix(prey_matrix[-c(1:4)])
rownames(m_p) <- prey_matrix$sample
m_p_hell <- decostand(m_p, 'hellinger') # Hellinger transformation
m_p_meta <- select(prey_matrix, sample, site, trap, trap_type) # metadata
# Scree plot to check stress per number of dimensions
dimcheckMDS(m_p_hell, distance = "bray", autotransform = FALSE, k = 10)
# Create NMDS ordination
ord <- metaMDS(m_p_hell,
distance = 'bray', # use Bray-Curtis distances
autotransform = FALSE, # already manually transformed the matrix
k = 3, # number of dimensions
trymax = 1000)
# Stress plot
stressplot(ord)
# Plot ordination
scale_trap <- c("red", "orange")
names(scale_trap) <- c("pitfall", "sticky")
par(mar = c(4,4,1,1))
plot(ord, display = 'species', type = 'n')
points(ord, display = 'sites', pch = 19, col = scale_trap[m_p_meta$trap_type])
ordiellipse(ord, groups = m_p_meta$trap_type,
label = TRUE, col = scale_trap, lwd = 2)
text(ord, display = 'species')
- Each dot is a trap
- Number of dimensions = 3
- Stress = 0.097
Notes & resources
SESSION INFO
devtools::session_info()## - Session info ------------------------------------------------------------------------------------------------
## setting value
## version R version 4.1.0 (2021-05-18)
## os Windows 10 x64
## system i386, mingw32
## ui RStudio
## language (EN)
## collate English_United States.1252
## ctype English_United States.1252
## tz America/Los_Angeles
## date 2021-07-28
##
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## [2] C:/Users/kathr/AppData/Local/Temp/RtmpYpCHWp/renv-system-library
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